Descriptive Stats
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library(AmesHousing)
ames = make_ames()
ames head(ames)# gives first six observations
# good for checking that all variables you want are in data set and for looking at type of variables str(ames)## tibble [2,930 × 81] (S3: tbl_df/tbl/data.frame)
## $ MS_SubClass : Factor w/ 16 levels "One_Story_1946_and_Newer_All_Styles",..: 1 1 1 1 6 6 12 12 12 6 ...
## $ MS_Zoning : Factor w/ 7 levels "Floating_Village_Residential",..: 3 2 3 3 3 3 3 3 3 3 ...
## $ Lot_Frontage : num [1:2930] 141 80 81 93 74 78 41 43 39 60 ...
## $ Lot_Area : int [1:2930] 31770 11622 14267 11160 13830 9978 4920 5005 5389 7500 ...
## $ Street : Factor w/ 2 levels "Grvl","Pave": 2 2 2 2 2 2 2 2 2 2 ...
## $ Alley : Factor w/ 3 levels "Gravel","No_Alley_Access",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ Lot_Shape : Factor w/ 4 levels "Regular","Slightly_Irregular",..: 2 1 2 1 2 2 1 2 2 1 ...
## $ Land_Contour : Factor w/ 4 levels "Bnk","HLS","Low",..: 4 4 4 4 4 4 4 2 4 4 ...
## $ Utilities : Factor w/ 3 levels "AllPub","NoSeWa",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Lot_Config : Factor w/ 5 levels "Corner","CulDSac",..: 1 5 1 1 5 5 5 5 5 5 ...
## $ Land_Slope : Factor w/ 3 levels "Gtl","Mod","Sev": 1 1 1 1 1 1 1 1 1 1 ...
## $ Neighborhood : Factor w/ 29 levels "North_Ames","College_Creek",..: 1 1 1 1 7 7 17 17 17 7 ...
## $ Condition_1 : Factor w/ 9 levels "Artery","Feedr",..: 3 2 3 3 3 3 3 3 3 3 ...
## $ Condition_2 : Factor w/ 8 levels "Artery","Feedr",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ Bldg_Type : Factor w/ 5 levels "OneFam","TwoFmCon",..: 1 1 1 1 1 1 5 5 5 1 ...
## $ House_Style : Factor w/ 8 levels "One_and_Half_Fin",..: 3 3 3 3 8 8 3 3 3 8 ...
## $ Overall_Qual : Factor w/ 10 levels "Very_Poor","Poor",..: 6 5 6 7 5 6 8 8 8 7 ...
## $ Overall_Cond : Factor w/ 10 levels "Very_Poor","Poor",..: 5 6 6 5 5 6 5 5 5 5 ...
## $ Year_Built : int [1:2930] 1960 1961 1958 1968 1997 1998 2001 1992 1995 1999 ...
## $ Year_Remod_Add : int [1:2930] 1960 1961 1958 1968 1998 1998 2001 1992 1996 1999 ...
## $ Roof_Style : Factor w/ 6 levels "Flat","Gable",..: 4 2 4 4 2 2 2 2 2 2 ...
## $ Roof_Matl : Factor w/ 8 levels "ClyTile","CompShg",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ Exterior_1st : Factor w/ 16 levels "AsbShng","AsphShn",..: 4 14 15 4 14 14 6 7 6 14 ...
## $ Exterior_2nd : Factor w/ 17 levels "AsbShng","AsphShn",..: 11 15 16 4 15 15 6 7 6 15 ...
## $ Mas_Vnr_Type : Factor w/ 5 levels "BrkCmn","BrkFace",..: 5 4 2 4 4 2 4 4 4 4 ...
## $ Mas_Vnr_Area : num [1:2930] 112 0 108 0 0 20 0 0 0 0 ...
## $ Exter_Qual : Factor w/ 4 levels "Excellent","Fair",..: 4 4 4 3 4 4 3 3 3 4 ...
## $ Exter_Cond : Factor w/ 5 levels "Excellent","Fair",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ Foundation : Factor w/ 6 levels "BrkTil","CBlock",..: 2 2 2 2 3 3 3 3 3 3 ...
## $ Bsmt_Qual : Factor w/ 6 levels "Excellent","Fair",..: 6 6 6 6 3 6 3 3 3 6 ...
## $ Bsmt_Cond : Factor w/ 6 levels "Excellent","Fair",..: 3 6 6 6 6 6 6 6 6 6 ...
## $ Bsmt_Exposure : Factor w/ 5 levels "Av","Gd","Mn",..: 2 4 4 4 4 4 3 4 4 4 ...
## $ BsmtFin_Type_1 : Factor w/ 7 levels "ALQ","BLQ","GLQ",..: 2 6 1 1 3 3 3 1 3 7 ...
## $ BsmtFin_SF_1 : num [1:2930] 2 6 1 1 3 3 3 1 3 7 ...
## $ BsmtFin_Type_2 : Factor w/ 7 levels "ALQ","BLQ","GLQ",..: 7 4 7 7 7 7 7 7 7 7 ...
## $ BsmtFin_SF_2 : num [1:2930] 0 144 0 0 0 0 0 0 0 0 ...
## $ Bsmt_Unf_SF : num [1:2930] 441 270 406 1045 137 ...
## $ Total_Bsmt_SF : num [1:2930] 1080 882 1329 2110 928 ...
## $ Heating : Factor w/ 6 levels "Floor","GasA",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ Heating_QC : Factor w/ 5 levels "Excellent","Fair",..: 2 5 5 1 3 1 1 1 1 3 ...
## $ Central_Air : Factor w/ 2 levels "N","Y": 2 2 2 2 2 2 2 2 2 2 ...
## $ Electrical : Factor w/ 6 levels "FuseA","FuseF",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ First_Flr_SF : int [1:2930] 1656 896 1329 2110 928 926 1338 1280 1616 1028 ...
## $ Second_Flr_SF : int [1:2930] 0 0 0 0 701 678 0 0 0 776 ...
## $ Low_Qual_Fin_SF : int [1:2930] 0 0 0 0 0 0 0 0 0 0 ...
## $ Gr_Liv_Area : int [1:2930] 1656 896 1329 2110 1629 1604 1338 1280 1616 1804 ...
## $ Bsmt_Full_Bath : num [1:2930] 1 0 0 1 0 0 1 0 1 0 ...
## $ Bsmt_Half_Bath : num [1:2930] 0 0 0 0 0 0 0 0 0 0 ...
## $ Full_Bath : int [1:2930] 1 1 1 2 2 2 2 2 2 2 ...
## $ Half_Bath : int [1:2930] 0 0 1 1 1 1 0 0 0 1 ...
## $ Bedroom_AbvGr : int [1:2930] 3 2 3 3 3 3 2 2 2 3 ...
## $ Kitchen_AbvGr : int [1:2930] 1 1 1 1 1 1 1 1 1 1 ...
## $ Kitchen_Qual : Factor w/ 5 levels "Excellent","Fair",..: 5 5 3 1 5 3 3 3 3 3 ...
## $ TotRms_AbvGrd : int [1:2930] 7 5 6 8 6 7 6 5 5 7 ...
## $ Functional : Factor w/ 8 levels "Maj1","Maj2",..: 8 8 8 8 8 8 8 8 8 8 ...
## $ Fireplaces : int [1:2930] 2 0 0 2 1 1 0 0 1 1 ...
## $ Fireplace_Qu : Factor w/ 6 levels "Excellent","Fair",..: 3 4 4 6 6 3 4 4 6 6 ...
## $ Garage_Type : Factor w/ 7 levels "Attchd","Basment",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Garage_Finish : Factor w/ 4 levels "Fin","No_Garage",..: 1 4 4 1 1 1 1 3 3 1 ...
## $ Garage_Cars : num [1:2930] 2 1 1 2 2 2 2 2 2 2 ...
## $ Garage_Area : num [1:2930] 528 730 312 522 482 470 582 506 608 442 ...
## $ Garage_Qual : Factor w/ 6 levels "Excellent","Fair",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Garage_Cond : Factor w/ 6 levels "Excellent","Fair",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Paved_Drive : Factor w/ 3 levels "Dirt_Gravel",..: 2 3 3 3 3 3 3 3 3 3 ...
## $ Wood_Deck_SF : int [1:2930] 210 140 393 0 212 360 0 0 237 140 ...
## $ Open_Porch_SF : int [1:2930] 62 0 36 0 34 36 0 82 152 60 ...
## $ Enclosed_Porch : int [1:2930] 0 0 0 0 0 0 170 0 0 0 ...
## $ Three_season_porch: int [1:2930] 0 0 0 0 0 0 0 0 0 0 ...
## $ Screen_Porch : int [1:2930] 0 120 0 0 0 0 0 144 0 0 ...
## $ Pool_Area : int [1:2930] 0 0 0 0 0 0 0 0 0 0 ...
## $ Pool_QC : Factor w/ 5 levels "Excellent","Fair",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ Fence : Factor w/ 5 levels "Good_Privacy",..: 5 3 5 5 3 5 5 5 5 5 ...
## $ Misc_Feature : Factor w/ 6 levels "Elev","Gar2",..: 3 3 2 3 3 3 3 3 3 3 ...
## $ Misc_Val : int [1:2930] 0 0 12500 0 0 0 0 0 0 0 ...
## $ Mo_Sold : int [1:2930] 5 6 6 4 3 6 4 1 3 6 ...
## $ Year_Sold : int [1:2930] 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 ...
## $ Sale_Type : Factor w/ 10 levels "COD","Con","ConLD",..: 10 10 10 10 10 10 10 10 10 10 ...
## $ Sale_Condition : Factor w/ 6 levels "Abnorml","AdjLand",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ Sale_Price : int [1:2930] 215000 105000 172000 244000 189900 195500 213500 191500 236500 189000 ...
## $ Longitude : num [1:2930] -93.6 -93.6 -93.6 -93.6 -93.6 ...
## $ Latitude : num [1:2930] 42.1 42.1 42.1 42.1 42.1 ...
## - attr(*, "spec")=List of 2
## ..$ cols :List of 82
## .. ..$ Order : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ PID : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ MS SubClass : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ MS Zoning : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Lot Frontage : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Lot Area : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Street : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Alley : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Lot Shape : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Land Contour : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Utilities : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Lot Config : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Land Slope : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Neighborhood : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Condition 1 : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Condition 2 : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Bldg Type : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ House Style : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Overall Qual : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Overall Cond : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Year Built : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Year Remod/Add : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Roof Style : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Roof Matl : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Exterior 1st : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Exterior 2nd : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Mas Vnr Type : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Mas Vnr Area : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Exter Qual : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Exter Cond : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Foundation : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Bsmt Qual : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Bsmt Cond : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Bsmt Exposure : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ BsmtFin Type 1 : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ BsmtFin SF 1 : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ BsmtFin Type 2 : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ BsmtFin SF 2 : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Bsmt Unf SF : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Total Bsmt SF : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Heating : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Heating QC : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Central Air : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Electrical : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ 1st Flr SF : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ 2nd Flr SF : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Low Qual Fin SF: list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Gr Liv Area : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Bsmt Full Bath : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Bsmt Half Bath : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Full Bath : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Half Bath : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Bedroom AbvGr : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Kitchen AbvGr : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Kitchen Qual : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ TotRms AbvGrd : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Functional : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Fireplaces : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Fireplace Qu : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Garage Type : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Garage Yr Blt : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Garage Finish : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Garage Cars : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Garage Area : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Garage Qual : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Garage Cond : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Paved Drive : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Wood Deck SF : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Open Porch SF : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Enclosed Porch : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ 3Ssn Porch : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Screen Porch : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Pool Area : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Pool QC : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Fence : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Misc Feature : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Misc Val : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Mo Sold : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Yr Sold : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## .. ..$ Sale Type : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Sale Condition : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ SalePrice : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_integer" "collector"
## ..$ default: list()
## .. ..- attr(*, "class")= chr [1:2] "collector_guess" "collector"
## ..- attr(*, "class")= chr "col_spec"# gives you the structure of the data
# Data types - how the data are stored in R
# Levels are categoriesMin and Max of data
# use $ and variable name
min(ames$Lot_Frontage)## [1] 0max(ames$Lot_Frontage)## [1] 313amesRange
range(ames$Lot_Area)## [1] 1300 215245Mean
mean(ames$Lot_Area)## [1] 10147.92Median
median(ames$Lot_Area)## [1] 9436.5## can also use quantile function
quantile(ames$Lot_Area, 0.5)## 50%
## 9436.5quantile(ames$Lot_Area, 0.25)## 25%
## 7440.25# can do with any quartiles Interquartile Range
# difference between first and third quartiles
IQR(ames$Lot_Area)## [1] 4115Standard Dev and Variance
sd(ames$Lot_Area)## [1] 7880.018var(ames$Lot_Area)## [1] 62094680## can also apply to multiple columns
# can also try : ames[,c(“lot_area”,”lot_frontage")
lapply(ames[, 3:4], sd)## $Lot_Frontage
## [1] 33.49944
##
## $Lot_Area
## [1] 7880.018# remember that lapply function applies a specific function to data --> i.e. (data you want, function you want)Really helpful summary function
summary(ames[, 3:4])## Lot_Frontage Lot_Area
## Min. : 0.00 Min. : 1300
## 1st Qu.: 43.00 1st Qu.: 7440
## Median : 63.00 Median : 9436
## Mean : 57.65 Mean : 10148
## 3rd Qu.: 78.00 3rd Qu.: 11555
## Max. :313.00 Max. :215245Mode
Unfortunately, there is no function in R to find the mode of a variable (that I know of). Here’s one way I know of to do it.
table_ames <- table(ames$Lot_Area) # number of occurrences for each unique value
sort(table_ames, decreasing = TRUE) # sort highest to lowest##
## 9600 7200 6000 9000 10800 7500 8400 1680 6240 6120 7000
## 44 43 34 29 25 21 21 18 18 17 13
## 8125 9100 9750 10400 5400 8000 8640 2280 5000 8450 8750
## 12 12 12 11 10 10 10 9 9 9 9
## 9900 10000 3182 7800 10320 4435 8800 9350 10140 10200 10625
## 9 9 8 8 8 7 7 7 7 7 7
## 11250 11700 1890 3675 4500 6600 7018 8250 8520 9375 11050
## 7 7 6 6 6 6 6 6 6 6 6
## 13072 7750 7875 8064 8544 8760 8777 10237 10440 10530 11500
## 6 5 5 5 5 5 5 5 5 5 5
## 1533 2117 2308 2522 2544 2645 2665 3180 4426 4800 5925
## 4 4 4 4 4 4 4 4 4 4 4
## 6762 7150 7175 7700 8012 8050 8172 8480 8500 8712 9060
## 4 4 4 4 4 4 4 4 4 4 4
## 9200 9316 9360 9525 9571 10125 10410 10500 11000 11235 11645
## 4 4 4 4 4 4 4 4 4 4 4
## 11767 12000 12090 13125 13891 2160 3600 3907 4043 4224 5330
## 4 4 4 4 4 3 3 3 3 3 3
## 5500 5520 6300 7560 7590 8010 8094 8100 8248 8749 8773
## 3 3 3 3 3 3 3 3 3 3 3
## 8814 9084 9120 9240 9317 9720 9760 9825 9920 9965 10206
## 3 3 3 3 3 3 3 3 3 3 3
## 11075 11200 11340 11475 11600 11625 11800 11900 12900 13300 13500
## 3 3 3 3 3 3 3 3 3 3 3
## 13600 13693 14115 14175 15600 21780 1477 1526 1596 1936 1974
## 3 3 3 3 3 3 2 2 2 2 2
## 2364 2448 2998 3000 3010 3072 3196 3230 3500 3710 3811
## 2 2 2 2 2 2 2 2 2 2 2
## 3842 3843 3880 3922 3940 3951 4060 4280 4438 4571 4590
## 2 2 2 2 2 2 2 2 2 2 2
## 4928 5160 5350 5680 5700 6060 6285 6292 6305 6360 6410
## 2 2 2 2 2 2 2 2 2 2 2
## 6876 6882 6900 6931 6960 7024 7153 7155 7162 7180 7288
## 2 2 2 2 2 2 2 2 2 2 2
## 7301 7350 7390 7400 7420 7703 7740 7758 7804 7830 7892
## 2 2 2 2 2 2 2 2 2 2 2
## 7917 7920 7931 8145 8158 8197 8212 8235 8239 8314 8385
## 2 2 2 2 2 2 2 2 2 2 2
## 8398 8402 8405 8410 8470 8499 8550 8635 8658 8660 8700
## 2 2 2 2 2 2 2 2 2 2 2
## 8740 8741 8769 8816 8846 8850 8872 8877 8892 8899 8923
## 2 2 2 2 2 2 2 2 2 2 2
## 9020 9042 9045 9069 9135 9142 9144 9178 9187 9245 9260
## 2 2 2 2 2 2 2 2 2 2 2
## 9337 9373 9400 9450 9500 9520 9535 9591 9605 9627 9638
## 2 2 2 2 2 2 2 2 2 2 2
## 9650 9660 9675 9724 9780 9786 9790 9800 9802 9803 9819
## 2 2 2 2 2 2 2 2 2 2 2
## 9839 9842 9928 9937 9938 10010 10050 10084 10110 10120 10134
## 2 2 2 2 2 2 2 2 2 2 2
## 10141 10152 10240 10307 10335 10382 10452 10475 10480 10560 10574
## 2 2 2 2 2 2 2 2 2 2 2
## 10603 10628 10667 10762 10778 10791 10820 10890 10928 10936 10950
## 2 2 2 2 2 2 2 2 2 2 2
## 11003 11025 11029 11040 11070 11088 11100 11184 11194 11275 11426
## 2 2 2 2 2 2 2 2 2 2 2
## 11556 11616 11643 11664 11778 11792 11851 11988 12099 12144 12150
## 2 2 2 2 2 2 2 2 2 2 2
## 12155 12160 12180 12205 12328 12342 12350 12400 12438 12450 12633
## 2 2 2 2 2 2 2 2 2 2 2
## 12665 12704 12720 12774 13108 13200 13418 13650 13680 13695 13758
## 2 2 2 2 2 2 2 2 2 2 2
## 14000 14137 14330 14450 14803 15431 15611 15660 15750 16659 16770
## 2 2 2 2 2 2 2 2 2 2 2
## 17500 17871 21750 1300 1470 1476 1484 1488 1491 1495 1504
## 2 2 2 1 1 1 1 1 1 1 1
## 1612 1700 1733 1782 1869 1879 1894 1900 1920 1950 1953
## 1 1 1 1 1 1 1 1 1 1 1
## 2001 2016 2058 2104 2179 2205 2217 2268 2289 2304 2349
## 1 1 1 1 1 1 1 1 1 1 1
## 2368 2394 2403 2500 2529 2572 2592 2628 2651 2760 2880
## 1 1 1 1 1 1 1 1 1 1 1
## 2887 2938 2980 3013 3068 3087 3136 3153 3203 3215 3242
## 1 1 1 1 1 1 1 1 1 1 1
## 3300 3316 3363 3378 3435 3480 3515 3523 3604 3606 3612
## 1 1 1 1 1 1 1 1 1 1 1
## 3621 3628 3635 3636 3640 3672 3684 3696 3701 3735 3760
## 1 1 1 1 1 1 1 1 1 1 1
## 3768 3782 3784 3830 3840 3869 3874 3876 3901 3903 3950
## 1 1 1 1 1 1 1 1 1 1 1
## 3960 3964 3982 4000 4017 4045 4054 4058 4080 4084 4109
## 1 1 1 1 1 1 1 1 1 1 1
## 4113 4118 4130 4217 4230 4235 4251 4270 4274 4282 4330
## 1 1 1 1 1 1 1 1 1 1 1
## 4347 4379 4385 4388 4400 4403 4420 4447 4456 4480 4484
## 1 1 1 1 1 1 1 1 1 1 1
## 4485 4538 4608 4671 4712 4740 4750 4761 4765 4835 4853
## 1 1 1 1 1 1 1 1 1 1 1
## 4882 4899 4920 4923 4960 5001 5005 5062 5063 5070 5100
## 1 1 1 1 1 1 1 1 1 1 1
## 5105 5118 5119 5122 5142 5150 5175 5190 5220 5232 5250
## 1 1 1 1 1 1 1 1 1 1 1
## 5271 5280 5306 5310 5362 5381 5389 5395 5436 5470 5568
## 1 1 1 1 1 1 1 1 1 1 1
## 5586 5587 5600 5604 5633 5664 5684 5687 5707 5720 5747
## 1 1 1 1 1 1 1 1 1 1 1
## 5748 5775 5784 5790 5805 5814 5820 5825 5830 5852 5858
## 1 1 1 1 1 1 1 1 1 1 1
## 5868 5870 5890 5900 5911 5914 5940 5950 5976 6001 6012
## 1 1 1 1 1 1 1 1 1 1 1
## 6040 6048 6125 6130 6155 6171 6173 6180 6191 6204 6221
## 1 1 1 1 1 1 1 1 1 1 1
## 6264 6270 6289 6291 6324 6342 6371 6373 6380 6390 6400
## 1 1 1 1 1 1 1 1 1 1 1
## 6402 6406 6420 6430 6435 6442 6449 6451 6472 6488 6490
## 1 1 1 1 1 1 1 1 1 1 1
## 6500 6563 6565 6615 6627 6629 6710 6718 6720 6723 6756
## 1 1 1 1 1 1 1 1 1 1 1
## 6760 6768 6780 6792 6820 6821 6845 6853 6854 6858 6860
## 1 1 1 1 1 1 1 1 1 1 1
## 6897 6904 6911 6930 6950 6951 6953 6955 6956 6970 6978
## 1 1 1 1 1 1 1 1 1 1 1
## 6979 6993 7006 7007 7008 7010 7015 7020 7023 7030 7032
## 1 1 1 1 1 1 1 1 1 1 1
## 7038 7040 7050 7052 7056 7060 7064 7082 7094 7100 7111
## 1 1 1 1 1 1 1 1 1 1 1
## 7128 7130 7132 7134 7136 7176 7207 7223 7226 7227 7230
## 1 1 1 1 1 1 1 1 1 1 1
## 7242 7244 7250 7252 7259 7264 7290 7296 7308 7311 7313
## 1 1 1 1 1 1 1 1 1 1 1
## 7314 7315 7321 7328 7332 7340 7360 7379 7380 7388 7392
## 1 1 1 1 1 1 1 1 1 1 1
## 7399 7404 7406 7407 7415 7424 7425 7436 7438 7440 7441
## 1 1 1 1 1 1 1 1 1 1 1
## 7446 7449 7450 7472 7476 7480 7488 7506 7514 7518 7535
## 1 1 1 1 1 1 1 1 1 1 1
## 7540 7550 7558 7570 7577 7584 7585 7588 7596 7599 7609
## 1 1 1 1 1 1 1 1 1 1 1
## 7614 7626 7627 7628 7630 7632 7635 7642 7655 7658 7669
## 1 1 1 1 1 1 1 1 1 1 1
## 7677 7681 7685 7689 7692 7697 7706 7711 7713 7728 7733
## 1 1 1 1 1 1 1 1 1 1 1
## 7741 7742 7745 7755 7763 7777 7785 7791 7793 7795 7801
## 1 1 1 1 1 1 1 1 1 1 1
## 7810 7819 7820 7822 7832 7837 7838 7840 7841 7844 7848
## 1 1 1 1 1 1 1 1 1 1 1
## 7851 7861 7862 7879 7890 7898 7903 7910 7915 7922 7930
## 1 1 1 1 1 1 1 1 1 1 1
## 7936 7937 7939 7942 7943 7945 7950 7976 7980 7984 7990
## 1 1 1 1 1 1 1 1 1 1 1
## 7993 8004 8013 8014 8020 8029 8035 8049 8063 8068 8070
## 1 1 1 1 1 1 1 1 1 1 1
## 8072 8076 8078 8088 8089 8092 8098 8118 8120 8121 8123
## 1 1 1 1 1 1 1 1 1 1 1
## 8127 8128 8139 8146 8147 8154 8155 8160 8163 8169 8170
## 1 1 1 1 1 1 1 1 1 1 1
## 8174 8176 8190 8198 8199 8200 8220 8229 8232 8238 8240
## 1 1 1 1 1 1 1 1 1 1 1
## 8243 8244 8246 8251 8263 8267 8280 8281 8285 8286 8294
## 1 1 1 1 1 1 1 1 1 1 1
## 8298 8300 8304 8308 8320 8333 8334 8335 8339 8340 8366
## 1 1 1 1 1 1 1 1 1 1 1
## 8368 8375 8382 8390 8393 8396 8413 8414 8425 8428 8430
## 1 1 1 1 1 1 1 1 1 1 1
## 8433 8445 8453 8461 8462 8471 8472 8475 8487 8510 8512
## 1 1 1 1 1 1 1 1 1 1 1
## 8513 8516 8521 8525 8529 8530 8532 8534 8536 8540 8546
## 1 1 1 1 1 1 1 1 1 1 1
## 8556 8562 8574 8577 8581 8593 8600 8604 8626 8633 8637
## 1 1 1 1 1 1 1 1 1 1 1
## 8638 8665 8668 8674 8680 8685 8688 8696 8702 8707 8723
## 1 1 1 1 1 1 1 1 1 1 1
## 8724 8726 8730 8731 8736 8737 8738 8755 8765 8767 8772
## 1 1 1 1 1 1 1 1 1 1 1
## 8775 8778 8780 8789 8791 8795 8803 8810 8820 8826 8834
## 1 1 1 1 1 1 1 1 1 1 1
## 8838 8842 8847 8849 8854 8856 8857 8880 8883 8885 8900
## 1 1 1 1 1 1 1 1 1 1 1
## 8910 8917 8918 8924 8925 8926 8927 8930 8935 8940 8944
## 1 1 1 1 1 1 1 1 1 1 1
## 8960 8963 8965 8967 8969 8970 8973 8978 8982 8987 8990
## 1 1 1 1 1 1 1 1 1 1 1
## 8991 8993 8998 9017 9018 9019 9022 9024 9037 9044 9056
## 1 1 1 1 1 1 1 1 1 1 1
## 9066 9073 9079 9085 9098 9101 9109 9116 9125 9129 9130
## 1 1 1 1 1 1 1 1 1 1 1
## 9139 9140 9143 9150 9156 9157 9158 9170 9171 9179 9180
## 1 1 1 1 1 1 1 1 1 1 1
## 9184 9196 9204 9205 9206 9215 9216 9230 9233 9236 9239
## 1 1 1 1 1 1 1 1 1 1 1
## 9246 9247 9248 9250 9254 9259 9262 9272 9278 9280 9286
## 1 1 1 1 1 1 1 1 1 1 1
## 9291 9297 9300 9303 9308 9313 9320 9340 9345 9353 9364
## 1 1 1 1 1 1 1 1 1 1 1
## 9370 9382 9392 9399 9405 9416 9428 9430 9434 9439 9452
## 1 1 1 1 1 1 1 1 1 1 1
## 9453 9457 9462 9464 9466 9468 9473 9477 9480 9482 9487
## 1 1 1 1 1 1 1 1 1 1 1
## 9488 9490 9492 9503 9505 9510 9512 9531 9532 9533 9541
## 1 1 1 1 1 1 1 1 1 1 1
## 9543 9545 9547 9548 9549 9550 9554 9555 9556 9560 9572
## 1 1 1 1 1 1 1 1 1 1 1
## 9576 9587 9588 9590 9610 9612 9620 9636 9639 9649 9656
## 1 1 1 1 1 1 1 1 1 1 1
## 9658 9662 9670 9671 9672 9680 9709 9717 9723 9729 9734
## 1 1 1 1 1 1 1 1 1 1 1
## 9735 9736 9738 9742 9743 9757 9758 9759 9763 9764 9765
## 1 1 1 1 1 1 1 1 1 1 1
## 9768 9770 9771 9783 9801 9808 9828 9830 9836 9840 9849
## 1 1 1 1 1 1 1 1 1 1 1
## 9855 9856 9858 9863 9873 9880 9888 9892 9906 9910 9926
## 1 1 1 1 1 1 1 1 1 1 1
## 9927 9930 9942 9945 9947 9950 9967 9978 9981 9986 9990
## 1 1 1 1 1 1 1 1 1 1 1
## 9991 10004 10005 10007 10011 10012 10019 10020 10021 10029 10032
## 1 1 1 1 1 1 1 1 1 1 1
## 10037 10041 10042 10044 10083 10090 10106 10114 10122 10126 10130
## 1 1 1 1 1 1 1 1 1 1 1
## 10142 10143 10147 10150 10159 10164 10170 10171 10172 10175 10176
## 1 1 1 1 1 1 1 1 1 1 1
## 10179 10180 10182 10184 10186 10192 10197 10205 10207 10208 10215
## 1 1 1 1 1 1 1 1 1 1 1
## 10226 10230 10235 10236 10246 10260 10261 10264 10265 10266 10267
## 1 1 1 1 1 1 1 1 1 1 1
## 10274 10284 10289 10295 10300 10304 10316 10324 10331 10337 10355
## 1 1 1 1 1 1 1 1 1 1 1
## 10356 10357 10364 10366 10367 10368 10380 10385 10386 10389 10395
## 1 1 1 1 1 1 1 1 1 1 1
## 10402 10411 10420 10421 10425 10429 10434 10437 10441 10447 10448
## 1 1 1 1 1 1 1 1 1 1 1
## 10454 10456 10457 10463 10464 10481 10482 10496 10512 10519 10532
## 1 1 1 1 1 1 1 1 1 1 1
## 10533 10541 10542 10544 10547 10552 10557 10562 10566 10570 10573
## 1 1 1 1 1 1 1 1 1 1 1
## 10592 10593 10594 10600 10612 10615 10616 10624 10632 10634 10635
## 1 1 1 1 1 1 1 1 1 1 1
## 10637 10646 10650 10652 10655 10656 10659 10665 10672 10678 10680
## 1 1 1 1 1 1 1 1 1 1 1
## 10682 10690 10708 10710 10712 10721 10725 10728 10732 10738 10739
## 1 1 1 1 1 1 1 1 1 1 1
## 10750 10751 10759 10768 10769 10773 10780 10784 10790 10793 10816
## 1 1 1 1 1 1 1 1 1 1 1
## 10818 10825 10832 10836 10839 10845 10846 10852 10858 10859 10872
## 1 1 1 1 1 1 1 1 1 1 1
## 10880 10895 10896 10899 10900 10905 10914 10918 10920 10921 10926
## 1 1 1 1 1 1 1 1 1 1 1
## 10927 10930 10933 10943 10944 10960 10970 10984 10990 10991 10994
## 1 1 1 1 1 1 1 1 1 1 1
## 10998 11002 11024 11027 11049 11058 11060 11064 11065 11067 11069
## 1 1 1 1 1 1 1 1 1 1 1
## 11072 11080 11084 11096 11103 11104 11105 11120 11128 11134 11136
## 1 1 1 1 1 1 1 1 1 1 1
## 11143 11146 11160 11166 11170 11175 11198 11202 11207 11210 11214
## 1 1 1 1 1 1 1 1 1 1 1
## 11216 11218 11227 11228 11241 11248 11249 11287 11302 11305 11308
## 1 1 1 1 1 1 1 1 1 1 1
## 11310 11316 11317 11327 11332 11333 11339 11341 11344 11345 11354
## 1 1 1 1 1 1 1 1 1 1 1
## 11355 11358 11361 11362 11367 11375 11380 11382 11388 11394 11400
## 1 1 1 1 1 1 1 1 1 1 1
## 11404 11409 11414 11419 11422 11423 11425 11428 11435 11443 11447
## 1 1 1 1 1 1 1 1 1 1 1
## 11449 11454 11457 11478 11479 11492 11512 11515 11520 11526 11553
## 1 1 1 1 1 1 1 1 1 1 1
## 11563 11577 11578 11584 11606 11613 11622 11631 11639 11646 11650
## 1 1 1 1 1 1 1 1 1 1 1
## 11660 11670 11672 11675 11677 11679 11690 11692 11694 11717 11727
## 1 1 1 1 1 1 1 1 1 1 1
## 11737 11750 11751 11762 11764 11765 11777 11782 11787 11796 11824
## 1 1 1 1 1 1 1 1 1 1 1
## 11825 11830 11836 11838 11839 11841 11844 11846 11850 11855 11875
## 1 1 1 1 1 1 1 1 1 1 1
## 11880 11883 11885 11888 11896 11911 11920 11923 11924 11927 11929
## 1 1 1 1 1 1 1 1 1 1 1
## 11932 11949 11950 11952 11957 11980 11999 12003 12011 12018 12030
## 1 1 1 1 1 1 1 1 1 1 1
## 12046 12048 12085 12095 12102 12104 12108 12118 12122 12128 12134
## 1 1 1 1 1 1 1 1 1 1 1
## 12137 12151 12168 12172 12182 12191 12192 12198 12203 12209 12216
## 1 1 1 1 1 1 1 1 1 1 1
## 12217 12220 12224 12227 12228 12243 12244 12250 12256 12257 12274
## 1 1 1 1 1 1 1 1 1 1 1
## 12285 12291 12292 12299 12304 12320 12327 12334 12352 12354 12358
## 1 1 1 1 1 1 1 1 1 1 1
## 12361 12366 12375 12376 12378 12384 12388 12392 12393 12394 12395
## 1 1 1 1 1 1 1 1 1 1 1
## 12416 12420 12435 12436 12444 12447 12456 12460 12461 12464 12469
## 1 1 1 1 1 1 1 1 1 1 1
## 12474 12493 12508 12511 12513 12518 12537 12539 12546 12552 12568
## 1 1 1 1 1 1 1 1 1 1 1
## 12585 12589 12606 12615 12640 12671 12677 12680 12692 12700 12702
## 1 1 1 1 1 1 1 1 1 1 1
## 12712 12728 12732 12735 12760 12772 12778 12782 12798 12800 12803
## 1 1 1 1 1 1 1 1 1 1 1
## 12822 12852 12853 12858 12864 12867 12878 12883 12886 12887 12888
## 1 1 1 1 1 1 1 1 1 1 1
## 12890 12891 12898 12919 12925 12929 12936 12961 12968 12984 13000
## 1 1 1 1 1 1 1 1 1 1 1
## 13001 13005 13006 13008 13014 13015 13031 13041 13050 13052 13053
## 1 1 1 1 1 1 1 1 1 1 1
## 13068 13069 13070 13101 13110 13128 13132 13142 13143 13159 13162
## 1 1 1 1 1 1 1 1 1 1 1
## 13173 13175 13204 13214 13215 13250 13253 13260 13262 13265 13284
## 1 1 1 1 1 1 1 1 1 1 1
## 13286 13339 13346 13350 13355 13360 13377 13383 13384 13400 13426
## 1 1 1 1 1 1 1 1 1 1 1
## 13438 13440 13450 13472 13474 13478 13495 13501 13514 13515 13517
## 1 1 1 1 1 1 1 1 1 1 1
## 13518 13526 13543 13560 13568 13587 13607 13615 13618 13641 13651
## 1 1 1 1 1 1 1 1 1 1 1
## 13654 13673 13682 13688 13700 13704 13710 13728 13751 13770 13774
## 1 1 1 1 1 1 1 1 1 1 1
## 13811 13825 13830 13837 13860 13869 13870 13907 13975 14006 14054
## 1 1 1 1 1 1 1 1 1 1 1
## 14067 14082 14100 14112 14122 14145 14149 14154 14157 14171 14190
## 1 1 1 1 1 1 1 1 1 1 1
## 14191 14200 14210 14215 14217 14226 14230 14235 14250 14257 14260
## 1 1 1 1 1 1 1 1 1 1 1
## 14267 14277 14299 14300 14303 14311 14331 14333 14357 14364 14375
## 1 1 1 1 1 1 1 1 1 1 1
## 14418 14419 14442 14463 14536 14541 14559 14565 14572 14584 14585
## 1 1 1 1 1 1 1 1 1 1 1
## 14587 14598 14601 14670 14680 14684 14694 14695 14720 14753 14762
## 1 1 1 1 1 1 1 1 1 1 1
## 14774 14778 14780 14781 14828 14836 14850 14859 14860 14892 14948
## 1 1 1 1 1 1 1 1 1 1 1
## 14963 14977 15038 15138 15218 15240 15256 15262 15263 15274 15295
## 1 1 1 1 1 1 1 1 1 1 1
## 15300 15306 15312 15384 15387 15400 15401 15410 15417 15426 15428
## 1 1 1 1 1 1 1 1 1 1 1
## 15498 15523 15564 15576 15578 15584 15593 15602 15623 15635 15676
## 1 1 1 1 1 1 1 1 1 1 1
## 15783 15810 15863 15865 15870 15896 15922 15957 16012 16023 16033
## 1 1 1 1 1 1 1 1 1 1 1
## 16052 16056 16059 16133 16157 16158 16163 16196 16219 16226 16259
## 1 1 1 1 1 1 1 1 1 1 1
## 16269 16280 16285 16287 16300 16321 16381 16387 16451 16466 16492
## 1 1 1 1 1 1 1 1 1 1 1
## 16500 16545 16560 16561 16635 16647 16669 16692 16698 16737 16779
## 1 1 1 1 1 1 1 1 1 1 1
## 16870 16900 16905 17043 17082 17104 17120 17140 17169 17199 17217
## 1 1 1 1 1 1 1 1 1 1 1
## 17227 17242 17360 17400 17423 17433 17485 17503 17529 17541 17542
## 1 1 1 1 1 1 1 1 1 1 1
## 17597 17600 17671 17755 17778 17808 17920 17979 18000 18030 18044
## 1 1 1 1 1 1 1 1 1 1 1
## 18062 18160 18261 18265 18275 18386 18450 18494 18559 18600 18800
## 1 1 1 1 1 1 1 1 1 1 1
## 18837 18890 19138 19255 19296 19378 19508 19522 19550 19645 19690
## 1 1 1 1 1 1 1 1 1 1 1
## 19800 19900 19950 19958 20000 20062 20064 20270 20355 20431 20544
## 1 1 1 1 1 1 1 1 1 1 1
## 20693 20781 20896 21000 21281 21286 21299 21370 21384 21453 21533
## 1 1 1 1 1 1 1 1 1 1 1
## 21535 21579 21695 21872 21930 22002 22136 22420 22692 22950 23257
## 1 1 1 1 1 1 1 1 1 1 1
## 23303 23580 23595 23730 23920 24090 24572 24682 25000 25095 25286
## 1 1 1 1 1 1 1 1 1 1 1
## 25339 25419 25485 26073 26142 26178 26400 27650 27697 28698 29959
## 1 1 1 1 1 1 1 1 1 1 1
## 31220 31250 31770 32463 32668 33120 33983 34650 35133 35760 36500
## 1 1 1 1 1 1 1 1 1 1 1
## 39104 39290 39384 40094 41600 43500 45600 46589 47007 47280 50102
## 1 1 1 1 1 1 1 1 1 1 1
## 50271 51974 53107 53227 53504 56600 57200 63887 70761 115149 159000
## 1 1 1 1 1 1 1 1 1 1 1
## 164660 215245
## 1 1# although the printout is long, we can see that the mode for lot area is 9600Counts
summary(ames$Bldg_Type)## OneFam TwoFmCon Duplex Twnhs TwnhsE
## 2425 62 109 101 233# gives you the number of each factor in the data Conditional Counts for Nominal/Ordinal Variables
# lets say we want to count the number of each "paved"
summary(ames$Paved_Drive)## Dirt_Gravel Partial_Pavement Paved
## 216 62 2652# another way to count the number of paved
sum(ames$Paved_Drive == "Paved")## [1] 2652# or condition
sum(ames$Paved_Drive == "Paved" | ames$Paved_Drive == "Dirt_Gravel")## [1] 2868# and condition
sum(ames$Paved_Drive == "Paved" & ames$Paved_Drive == "Dirt_Gravel")## [1] 0sum(ames$Paved_Drive == "Paved" & ames$Alley == "No_Alley_Access")## [1] 2518Conditional Counts for Numeric Variables
# find the number of lots greater than 1000
sum(ames$Lot_Area > 1000, na.rm=TRUE)## [1] 2930# na.rm = TRUE removes missing values for you # find the number of lots between 1000 and 2000
sum(ames$Lot_Area > 1000 & ames$Lot_Area < 2000, na.rm=TRUE)## [1] 57library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionames2 = ames %>% mutate(filtered_area = ifelse(Lot_Area > 10000 & Lot_Area < 20000, 1, 0))%>%select(filtered_area, everything())
ames2# now we have a variable at the end of our data set with this condition present library(dplyr)
ames2 = ames %>% mutate(filtered_area = ifelse(Lot_Area > 10000 & Lot_Area < 20000, 1, 0))%>%filter(filtered_area ==1)
ames2# now we have a variable at the end of our data set with this condition presentBarplot
barplot(table(ames$Lot_Shape)) # table() is mandatory
# really basic automatic barplot in R # more fancy, customizable barplot in R
library(ggplot2)
ggplot(ames, aes(x = Lot_Shape, fill = Lot_Shape)) +
geom_bar() +
ggtitle("Lot Shape of Houses in Ames") +
xlab("Lot Shape") + ylab("Number of Houses")
Line Plot
plot(ames$Lot_Area,
type = "l"
) # "l" for line
Histogram
hist(ames$Lot_Frontage)
## can do same in ggplot
ggplot(ames) +
aes(x = Lot_Frontage) +
geom_histogram(bins = 15)
# can change number of bins Boxplot
boxplot(ames$Lot_Frontage)
# side by side comparison of numerical to categorical
boxplot(ames$Lot_Frontage ~ ames$Alley)
ggplot(ames) +
aes(x = Alley, y = Lot_Frontage) +
geom_boxplot()
Scatterplot
amesplot(ames$Lot_Area, ames$Gr_Liv_Area)
ggplot(ames) +
aes(x = ames$Lot_Area, y = ames$Gr_Liv_Area) +
geom_point()## Warning: Use of `ames$Lot_Area` is discouraged. Use `Lot_Area` instead.## Warning: Use of `ames$Gr_Liv_Area` is discouraged. Use `Gr_Liv_Area` instead.
# add in a categorical factor for more info
ggplot(ames) +
aes(x = ames$Lot_Area, y = ames$Gr_Liv_Area, color = Alley) +
geom_point() +
scale_color_hue()## Warning: Use of `ames$Lot_Area` is discouraged. Use `Lot_Area` instead.## Warning: Use of `ames$Gr_Liv_Area` is discouraged. Use `Gr_Liv_Area` instead.
QQplot for Normality Assumptions
# Draw points on the qq-plot:
qqnorm(ames$Lot_Area)
# Draw the reference line:
qqline(ames$Lot_Area)
We can see that this deviates a lot from normality (the theoretical line of normal data), so this normality assumption would be broken.
Density plots for distribution
plot(density(ames$Lot_Area))